Abnormal Event Detection in Videos using Spatiotemporal Autoencoder (2017-01-06T00:00:00.000000Z)

TL;DR

This work proposes a spatiotemporal architecture for anomaly detection in videos including crowded scenes that includes two main components, one for spatial feature representation, and one for learning the temporal evolution of the spatial features.

Abstract

We present an efficient method for detecting anomalies in videos. Recent applications of convolutional neural networks have shown promises of convolutional layers for object detection and recognition, especially in images. However, convolutional neural networks are supervised and require labels as learning signals. We propose a spatiotemporal architecture for anomaly detection in videos including crowded scenes. Our architecture includes two main components, one for spatial feature representation, and one for learning the temporal evolution of the spatial features. Experimental results on Avenue, Subway and UCSD benchmarks confirm that the detection accuracy of our method is comparable to state-of-the-art methods at a considerable speed of up to 140 fps.

Authors

Yong Haur Tay

2 papers

Yong Shean Chong

1 papers

TL;DR

Abstract

Authors

References35 items

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